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arXiv · 1604.05096

Pixel-level Encoding and Depth Layering for Instance-level Semantic Labeling

Jonas Uhrig·Marius Cordts·Uwe Franke·Thomas Brox
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Abstract

Recent approaches for instance-aware semantic labeling have augmented convolutional neural networks (CNNs) with complex multi-task architectures or computationally expensive graphical models. We present a method that leverages a fully convolutional network (FCN) to predict semantic labels, depth and an instance-based encoding using each pixel's direction towards its corresponding instance center. Subsequently, we apply low-level computer vision techniques to generate state-of-the-art instance segmentation on the street scene datasets KITTI and Cityscapes. Our approach outperforms existing works by a large margin and can additionally predict absolute distances of individual instances from a monocular image as well as a pixel-level semantic labeling.

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Jonas Uhrig, Marius Cordts, Uwe Franke, Thomas Brox. 2016-04-18. Pixel-level Encoding and Depth Layering for Instance-level Semantic Labeling. https://arxiv.org/abs/1604.05096

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